### Abstract

We present a novel problem formulation and algorithm for H^{∞} system identification based on a stochastic noise model and constrained model set to reduce the conservatism in deterministic noise models, and statistical inefficiency and computational complexity associated with high-order estimates. By establishing a connection between a minimax problem and a sequence of weighted least square problems, we show that the proposed stochastic, constrained problem can be solved with a computationally attractive and conceptually simple iteratively weighted least square (IWLS) identification algorithm. The IWLS procedure is based on a sequence of standard parametric weighted least square output error identification routines, where the weighting is updated via non-parametric estimation of the modeling error to asymptotically achieve the H^{∞} identification criterion.

Original language | English (US) |
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Title of host publication | Proceedings of the IEEE Conference on Decision and Control |

Publisher | IEEE |

Pages | 3374-3379 |

Number of pages | 6 |

Volume | 4 |

State | Published - 1994 |

Event | Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA Duration: Dec 14 1994 → Dec 16 1994 |

### Other

Other | Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) |
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City | Lake Buena Vista, FL, USA |

Period | 12/14/94 → 12/16/94 |

### Fingerprint

### ASJC Scopus subject areas

- Chemical Health and Safety
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality

### Cite this

*Proceedings of the IEEE Conference on Decision and Control*(Vol. 4, pp. 3374-3379). IEEE.

**Stochastic H∞ identification : an iteratively weighted least squares algorithm.** / Rangan, Sundeep; Ren, Wei.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the IEEE Conference on Decision and Control.*vol. 4, IEEE, pp. 3374-3379, Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4), Lake Buena Vista, FL, USA, 12/14/94.

}

TY - GEN

T1 - Stochastic H∞ identification

T2 - an iteratively weighted least squares algorithm

AU - Rangan, Sundeep

AU - Ren, Wei

PY - 1994

Y1 - 1994

N2 - We present a novel problem formulation and algorithm for H∞ system identification based on a stochastic noise model and constrained model set to reduce the conservatism in deterministic noise models, and statistical inefficiency and computational complexity associated with high-order estimates. By establishing a connection between a minimax problem and a sequence of weighted least square problems, we show that the proposed stochastic, constrained problem can be solved with a computationally attractive and conceptually simple iteratively weighted least square (IWLS) identification algorithm. The IWLS procedure is based on a sequence of standard parametric weighted least square output error identification routines, where the weighting is updated via non-parametric estimation of the modeling error to asymptotically achieve the H∞ identification criterion.

AB - We present a novel problem formulation and algorithm for H∞ system identification based on a stochastic noise model and constrained model set to reduce the conservatism in deterministic noise models, and statistical inefficiency and computational complexity associated with high-order estimates. By establishing a connection between a minimax problem and a sequence of weighted least square problems, we show that the proposed stochastic, constrained problem can be solved with a computationally attractive and conceptually simple iteratively weighted least square (IWLS) identification algorithm. The IWLS procedure is based on a sequence of standard parametric weighted least square output error identification routines, where the weighting is updated via non-parametric estimation of the modeling error to asymptotically achieve the H∞ identification criterion.

UR - http://www.scopus.com/inward/record.url?scp=0028738674&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028738674&partnerID=8YFLogxK

M3 - Conference contribution

VL - 4

SP - 3374

EP - 3379

BT - Proceedings of the IEEE Conference on Decision and Control

PB - IEEE

ER -